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目录Generative Recommendation with Semantic IDs: A Practitioner’s HandbookTL;DRMethodExperimentQ&A总结与思考相关链接 Generative Recommendation with Semantic IDs: 阅读全文
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目录UniME-V2: MLLM-as-a-Judge for Universal Multimodal Embedding LearningTL;DRMethodMLLM-as-a-Judge for Hard Negatives MiningMLLM Judgment Based Trainin 阅读全文
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目录Qwen2.5-VL Technical ReportTL;DRMethodFast and Efficient Vision EncoderMRoPE对齐绝对时间信息Pre-TrainingInterleaved Image-Text DataGrounding Data with Absol 阅读全文
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目录SAIL-Embedding Technical Report: Omni-modal Embedding Foundation ModelTL;DRDataRecommendation-aware Data ConstructionDynamic Hard Negative MiningQ:动 阅读全文
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目录VLM2Vec-V2: Advancing Multimodal Embedding for Videos, Images, and Visual DocumentsTL;DRMethodQ:VLM2Vec-V2与原始VLM2Vec算法有什么区别?BenchmarkQ&AQ:CLS, QA, R 阅读全文